Method of determining depth of chest compressions during CPR

ABSTRACT

A method of processing a raw acceleration signal, measured by an accelerometer-based compression monitor, to produce an accurate and precise estimated actual depth of chest compressions. The raw acceleration signal is filtered during integration and then a moving average of past starting points estimates the actual current starting point. An estimated actual peak of the compression is then determined in a similar fashion. The estimated actual starting point is subtracted from the estimated actual peak to calculate the estimated actual depth of chest compressions. In addition, one or more reference sensors (such as an ECG noise sensor) may be used to help establish the starting points of compressions. The reference sensors may be used, either alone or in combination with other signal processing techniques, to enhance the accuracy and precision of the estimated actual depth of compressions.

This application is a continuation of U.S. application Ser. No.10/844,660 filed May 12, 2004 now U.S. Pat. No. 7,122,014, which is acontinuation of U.S. application Ser. No. 10/280,220 filed Oct. 25,2002, now U.S. Pat. No. 6,827,695.

FIELD OF THE INVENTIONS

The methods and devices described below relate to the field ofcardio-pulmonary resuscitation (CPR).

BACKGROUND OF THE INVENTIONS

The American Heart Association guidelines for the correct application ofcardio-pulmonary resuscitation (CPR) specify that chest compressions beperformed at the rate of 80 to 100 per minute and at a depth, relativeto the spine, of 1.5 to 2.0 inches. (Guidelines 2000 for CardiopulmonaryResuscitation and Emergency Cardiovascular Care, 102 Circulation Supp. I(2000).) However, CPR is physically and emotionally challenging, evenfor trained professionals. Research has shown that manual chestcompressions rarely meet the guidelines. See, for example, Ochoa et al.,The Effect of Rescuer Fatigue on the Quality of Chest Compressions,Resuscitation, vol. 37, p. 149-52. See also Hightower et al., Decay inQuality of Closed-Chest Compressions over Time, Ann Emerg Med,26(3):300-333, September 1995. One of the difficulties of performingcorrect chest compressions is that the rescuer imprecisely judges thetiming and depth of compressions, particularly when the rescuer becomestired. Thus, if accurate and timely user feedback could be provided tothe rescuer then the rescuer would be more likely to perform CPRcorrectly.

Various devices have been proposed to assist a rescuer in properlyapplying CPR. For example, Kelley, Apparatus for Assisting in theApplication of Cardiopulmonary Resuscitation, U.S. Pat. No. 5,496,257(Mar. 5, 1996) shows a device that uses a pressure sensor to monitorcompression forces and timing. Groenke et al., AED with Force Sensor,U.S. Pat. No. 6,125,299 (Sep. 26, 2000) shows a device that uses a forcesensor to measure the compression force applied to a patient's chest.However, these devices only measure the force applied to the chest anddo not measure the actual depth of compressions. A given force cancompress the chests of different patients by different amounts, someasuring only force will not provide sufficient or consistent feedbackto the rescuer. In addition, force-based measurements may also beinaccurate because of intra-patient variation in thoracic morphology andcompliance (stiffness).

CPR devices that use only accelerometers to measure depth ofcompressions, other than our own patented device shown in Halperin etal., CPR Chest Compression Monitor, U.S. Pat. No. 6,390,996 (May 21,2002), do not fully or accurately account for errors in the measuredacceleration; nor do they account for drift in the starting points ofcompressions. In addition, the integration process necessary to derivethe depth of compressions greatly compounds any errors in the measuredacceleration.

It is important to correct for errors in the measured acceleration sincethe total depth of compressions should be within the relatively narrowrange of 1.5 inches to 2.0 inches. Numerical simulations have shown thata total error in acceleration as small as 0.02 in/sec² results in anerror of 0.25 inches in displacement. Given the narrow depth range ofoptimal compressions, an error of 0.25 inches is unacceptable. Forexample, Freeman, Integrated Resuscitation, U.S. Publication2001/0047140 (Nov. 29, 2001) shows a device that uses an accelerometeras a compression sensor and mentions gauging chest depth with theaccelerometer. However, Freeman enables no method to account for theerrors inherent in using an accelerometer alone. Thus any measurementFreeman makes of chest compression depth is inaccurate.

Myklebust et al., System for Measuring and Using Parameters During ChestCompression in a Life-Saving Situation or a Practice Situation and AlsoApplication Thereof, U.S. Pat. No. 6,306,107 (Oct. 23, 2001) describes adevice which uses a pressure pad, containing an accelerometer and aforce activated switch, to determine the depth of depressions. However,Myklebust does not provide a means to measure compression depth using anaccelerometer alone, nor does Myklebust account for some kinds of errorin the measured value of chest compression depth (such as drift).

The problems inherent in the above devices show the difficulty ofsolving the problem of measuring chest compression depth using only anaccelerometer. Nevertheless, the basic concept of determiningdisplacement from a measured acceleration is straightforward (in asystem with a known starting position). Displacement is determined bydouble integrating the measured acceleration.

However, this method of measuring chest compression depth is complicatedby at least three major sources of error: signal error, externalacceleration error, and drift in the actual or measured starting pointsof compressions from the initial starting point of compressions. Signalerror comprises errors in the measured acceleration due to electronicnoise, the shaking of wires or cables, errors inherent in theaccelerometer, and other sources of noise in the acceleration itself.

External acceleration error comprises errors introduced by accelerationsapplied to the patient and/or the accelerometer other than accelerationscaused by CPR. For example, if the patient is being transported in anambulance and a rescuer is applying manual CPR with a compressionmonitor, then the accelerometer will measure accelerations caused byroad vibrations as well as accelerations caused by CPR. (If theambulance hits a pot hole then a large spike may appear in thecompression waveform.) The accelerometer, by itself, cannot distinguishbetween the accelerations caused by road noise and the accelerationscaused by compressions. In other words, the accelerometer measures acombined acceleration and not just the accelerations caused bycompressions. Accordingly, the compression monitor will report adisplacement value different from the actual chest displacement.

Another source of error, drift, comprises systematic shifts in theactual or reported starting points of each compression over an entireseries of compressions. The accelerometer has no “memory” of the initialstarting position. Thus, as the rescuer applies compressions thereported depth waveform can start to drift. The compression monitor mayindicate that the reported depth waveform is increasingly deeper thanthe actual waveform. This form of drift is referred to as positivedrift. On the other hand, drift can also cause the compression monitorto report a depth waveform that is increasingly more shallow than theactual waveform. In other words, actual compression starting points arebecoming increasingly deeper, but the compression monitor insteadreports each starting point as close to the initial starting point. Thisform of drift is referred to as negative drift.

One cause of negative drift is a failure to allow the chest to return toa fully relaxed position. Absent correction, the accelerometer willbegin measuring displacement from the new “initial” position. Thus, thecompression monitor erroneously informs the rescuer that the currentstarting point is at the initial starting point. However, the actualdepth of the current starting point is more than the depth reported bythe compression monitor. As a result, the rescuer may compress the chestharder than he should to achieve the erroneous depth suggested by thecompression monitor.

Another source of both types of drift is a change in the overallposition of the accelerometer with respect to the patient. For example,if the accelerometer is not fully secured then the accelerometer maysystematically slip. (This may also cause external acceleration error.)Yet another source of drift is expansion and contraction of the chestdue to ventilation performed simultaneously with compressions. Othersources of drift may also exist. Each source of drift may be independentof the others and may not cancel each other out, so the compressionmonitor should be able to account for both positive and negative drift.

Notwithstanding drift resulting from erroneous operation, changes in theactual starting point of compressions do occur. For example, if one ormore ribs break during CPR then the actual starting point of eachcompression may be closer to the spine (a phenomena known as chestremodeling). Other types of chest injury or disease that affect thestructure and strength of the rib cage can also cause chest remodeling.Chest remodeling can be gradual, in which case a gradual shift occurs inthe actual initial starting point of compressions. A compression monitorshould be able to account for the difference between erroneous drift andan actual shift in the starting points of compressions.

These and other sources of error are compounded by integrating theacceleration. The errors caused by signal noise and drift cause theconstants of integration to have a value other than zero. The non-zeroconstants of integration compound the errors already present in theacceleration. Thus, the total compression depth reported by thecompression monitor can be very inaccurate. Accordingly, methods areneeded to accurately and precisely derive the depth of chestcompressions from a measured acceleration.

SUMMARY

The methods and devices described below provide for signal processingtechniques that precisely and accurately derive the depth of chestcompressions from a measured acceleration of chest compressions.Specifically, the methods and devices provided below provide for a meansto correct chest displacement errors caused by signal error, externalacceleration error, and drift. According to one method, a moving averagetechnique is used to produce an accurate measurement of compressiondepth. According to a second method, a change in the patient's ECG(electrocardiogram) may be used to determine the starting points ofcompressions. These methods may be combined together to further increasethe accuracy of chest depth measurement.

In broad terms, a moving average technique averages a plurality ofcompression cycles together, but weights recent compressions moreheavily than compressions further in the past. One moving averagetechnique begins with filtering a raw acceleration signal to eliminateas much signal noise as practicable. The filtered acceleration signal isthen integrated to derive the velocity of compressions. The velocity isfiltered to remove accumulated low frequency variations. The filteredvelocity measurement is integrated again to derive chest displacement.Chest displacement is then processed through a baseline limiter and apeak limiter; the baseline limiter may comprise a moving averageprocessor and the peak limiter may comprise a moving average processor.The baseline limiter estimates the actual starting point of the currentcompression and the peak limiter estimates the actual peak depth of thecurrent compression. A baseline detector then identifies the startingpoint of the current compression. A peak detector then identifies thepeak depth of the current compression. A means for combining signalsthen combines the estimated starting point and the estimated peak depthto derive the estimated actual depth of the current compression.Finally, the estimated actual depth of the current compression isprovided to one or more devices which provide intelligible feedback to amanual CPR provider, to an automated CPR device, or to an ECG operator.

In another method, a change in the noise component of the patient's ECGis correlated to the start of a chest compression. When the noisecomponent of the patient's ECG signal exceeds a pre-determined thresholdthen the accelerometer begins to measure acceleration. Thus, the actualstarting point of the current compression is established. This methodreduces some forms of external acceleration error and reduced drift. Themethod also helps to set the constants of integration to zero.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a patient and an accelerometer-based compression monitor inplace on a patient.

FIG. 2 shows a graph of compression depth over time before signalprocessing, where compression depth is derived from a measuredacceleration.

FIG. 3 shows a graph of compression velocity over time before signalprocessing, where compression velocity is derived from a measuredacceleration.

FIG. 4 shows a graph of compression acceleration over time before signalprocessing, where compression acceleration is measured by anaccelerometer.

FIG. 5 is a flow chart of a signal processing technique that converts araw compression acceleration into an estimated actual compression depth.

FIG. 6 is a flow chart of an alternate signal processing technique thatconverts a raw compression acceleration into an estimated actualcompression depth.

FIG. 7 shows the graph of compression depth over time after filteringthe raw acceleration.

FIG. 8 shows the graph of compression velocity over time after filteringthe raw acceleration.

FIG. 9 shows the graph of compression acceleration over time afterfiltering the raw acceleration.

FIG. 10 shows the graph of compression depth over time after filteringboth the raw acceleration and the derived velocity.

FIG. 11 shows the graph of compression velocity over time afterfiltering both the raw acceleration and the derived velocity.

FIG. 12 shows the graph of compression acceleration over time afterfiltering the raw acceleration.

FIG. 13 shows the graph of compression depth over time after filteringboth the raw acceleration and the derived velocity, and after applying abaseline limiter to the compression depth waveform.

FIG. 14 shows the graph of compression velocity over time afterfiltering both the raw acceleration and the derived velocity, and afterapplying the baseline limiter to the compression velocity waveform.

FIG. 15 shows the graph of compression acceleration over time afterfiltering the raw acceleration and after applying the baseline limiterto the compression acceleration waveform.

FIG. 16 shows the graph of compression depth over time after filteringboth the raw acceleration and the derived velocity, and after applyingthe baseline limiter and the peak limiter to the compression depthwaveform.

FIG. 17 shows the graph of compression velocity over time afterfiltering both the raw acceleration and the derived velocity, and afterapplying the baseline limiter and the peak limiter to the compressionvelocity waveform.

FIG. 18 shows the graph of compression acceleration over time afterfiltering the raw acceleration and after applying the baseline limiterand the peak limiter to the compression acceleration waveform.

FIG. 19 is a flow chart of a signal processing technique that uses achange in ECG noise to activate a switch which, in turn, controls whenan accelerometer begins to measure acceleration.

FIG. 20 shows a graph of compression depth over time before signalprocessing and with a negative drift in the reported compression depthwaveform.

FIG. 21 shows a graph of compression velocity over time before signalprocessing and with a negative drift in the reported compressionvelocity waveform.

FIG. 22 shows a graph of compression acceleration over time beforesignal processing and with a negative drift in the reported compressionacceleration waveform.

FIG. 23 shows the graph of FIG. 20 corrected by using a change in ECGnoise to establish the actual starting points of compressions.

FIG. 24 shows the graph of FIG. 21 corrected by using a change in ECGnoise to establish the actual starting points of compressions.

FIG. 25 shows the graph of FIG. 22 corrected by using a change in ECGnoise to establish the actual starting points of compressions.

FIG. 26 shows an accelerometer-based compression monitor in place on apatient and a system of reference sensors comprising a referenceaccelerometer, a switch, and a load sensor disposed such that eachsensor may measure various parameters related to chest compressions.

FIG. 27 illustrates a compression waveform that a user feedback systemmay prompt the rescuer to perform.

FIG. 28 is a block diagram of how an actual chest compressionacceleration is converted into a corrupted value for chest position.

FIG. 29 is a block diagram of a general solution for converting acorrupted chest compression acceleration into an estimated actual depthof chest compressions.

FIG. 30 is a block diagram of how an actual ECG signal is converted intoa corrupted ECG signal.

FIG. 31 is a block diagram of a general solution for converting a motioncorrupted ECG signal into an estimated actual ECG signal.

FIG. 32 is a graph of a pig's ECG signal that is corrupted by noisecaused by chest compressions.

FIG. 33 is a graph of CPR motion where CPR is performed on a pig.

FIG. 34 is a graph of the pig's estimated ECG noise signal.

FIG. 35 is a graph of the pig's estimated actual ECG signal.

DETAILED DESCRIPTION OF THE INVENTIONS

FIG. 1 shows a patient 1 and an accelerometer-based compression monitor2 in place on the patient. An accelerometer-based compression monitoruses one or more accelerometers to determine the depth of compressions.An example of an accelerometer-based compression monitor may be found inour own patent, Halperin et al., CPR Chest Compression Monitor, U.S.Pat. No. 6,390,996 (May 21, 2002), which is hereby incorporated byreference in its entirety. The compression monitor 2 is placed on thesternum 3 of the patient 1, on the rescuer's hands or arms, or on anautomatic CPR device. The chest is then compressed. The accelerometermeasures the acceleration of compressions and a processor 4 estimatesthe actual displacement of the accelerometer based on the measuredacceleration. The signal processing techniques described below ensurethat the estimated actual displacement is accurate and precise.

The estimated actual displacement may be provided to a displacementdisplay 5 that provides intelligible feedback to a manual CPR provideror to an automated CPR device. Likewise, other CPR-related parametersmay be provided to one or more compression device displays 6 (or othermeans for user feedback). CPR-related parameters include the depth ofchest compressions, the velocity of chest compressions, the accelerationof chest compressions, and the patient's ECG.

In the case of the patient's ECG, the compression monitor may beprovided with one or more electrodes. The processor may process thepatient's ECG during compressions to produce an estimated actual ECG.The estimated actual ECG may then be provided to an ECG display 7 (orother means for user feedback) that provides intelligible feedback tothe manual CPR provider, to an automated CPR device, or to otherindividuals or devices that monitor the patient's ECG.

The following terms are used throughout the specification and aredefined as follows:

Actual compression depth: the actual depth of a compression at any giventime.

Actual starting point of a compression: the actual place or point atwhich a chest compression begins.

Autoregressive moving average: a function that uses past data samples tomodify the current data sample.

Baseline portion of the compression depth waveform: that portion ofdepth waveform where the set of actual starting points is most likely tobe found.

Baseline limiter: a processor or function that operates on the baselineportion of the compression depth waveform.

Compression Peak: the place or point where maximum compression depthoccurs.

Current compression depth: the depth of a compression at any given time.

Current starting points: the starting point of the current compression.

Depth of compressions: the depth the chest is compressed at any instantin time, where depth is measured relative to the relaxed position of thechest.

Estimated actual starting point of a compression: the estimated value ofthe actual place or point at which a chest compression begins.

Initial starting point of compressions: the place or point at which aseries of compressions begins.

Measured starting point of a compression: the measured value of theplace or point at which a chest compression begins.

Moving average: a function that uses past data samples to modify thecurrent data sample.

Past starting points: the starting points of compressions that havealready occurred.

Peak portion of the compression depth waveform: that portion of depthwaveform where the set of actual peaks are most likely to be found.

Starting point of a compression: the place or point at which a chestcompression is begun.

FIGS. 2 through 4 show graphs of compression depth, velocity, andacceleration over time for four hypothetical compressions. No signalprocessing has been applied to any of waveforms shown in FIGS. 2 through4. Compression depth in FIG. 2 is shown as a positive value—the higherthe value, the deeper the chest has been compressed. The phantomwaveforms 12 represent the actual waveforms for compression depth,velocity, and acceleration (measured independently of theaccelerometer). The solid waveforms 13 represents the waveforms derivedfrom the acceleration measured by the compression monitor accelerometer.The waveforms 13 are also the waveforms reported by the compressionmonitor to the signal processing system 4. Compression depth is measuredin inches, marked at 1 inch intervals, compression velocity is measuredin inches per second (in/s), marked at 1 in/s intervals, and compressionacceleration is measured in inches per second per second (in/s²), markedat 1 in/s² intervals. For all three Figures time is measured in seconds,marked at 1 second intervals. The start of compressions is at time equalto zero. The initial depth of compressions is at depth equal to zero.

Phantom lines 14 and 15 intersect all three graphs. Phantom line 14corresponds to the time at which maximum compression depth is obtained.Phantom line 15 corresponds to the time at which minimum compressiondepth is obtained. In addition, phantom line 14 indicates that acompression depth maximum 16 corresponds to a compression velocity ofzero. Phantom line 14 also indicates that an acceleration maximum 17 isslightly offset from the compression depth maximum 16. Likewise, phantomline 15 indicates that a compression minimum 18 (or starting point orzero point) corresponds to a compression velocity of zero. Phantom line15 also indicates that an acceleration minimum 19 is slightly offsetfrom the compression depth minimum 18. A compression velocity maximum 20and minimum 21 occur around the middle of a compression.

The solid waveforms show the effects of three major types of error:signal error, external acceleration error, and drift. Signal error isprimarily represented by the “noisy” (rough) nature of the solidwaveforms; however, external acceleration error can also form a portionof the “noise.” Although the acceleration waveform is less noisy,integrating the acceleration increases the effect of the noise in thevelocity waveform. Integrating the velocity waveform increases theeffect of the noise yet again. Thus, the compression depth noise FIG. 2is higher than the compression velocity noise in FIG. 3, which is inturn higher than the compression acceleration noise in FIG. 4.Accordingly, the compression monitor will report a very noisycompression depth waveform.

External acceleration error is primarily represented by the large,positive spike 22 in the solid waveforms of FIGS. 2 through 4. (Althoughthe spike in FIGS. 2 through 4 occurs at a maximum, spikes can occuranywhere in the compression cycle and can affect the measuredacceleration both positively and negatively). The spike is caused by alarge acceleration unrelated to compressions, but nevertheless measuredby the accelerometer. Thus, the actual waveform 12 in all three figuresshows a corresponding peak 23 significantly below spike 22. Accordingly,absent the correction suggested here, the compression monitor willreport for that compression cycle a compression depth much higher thanthe actual compression depth.

Drift is primarily represented by the increasing distance between therespective minimums of the actual and reported waveforms of FIGS. 2through 4, as shown by arrows 24 and 25. The drift is causing thecompression monitor to erroneously report a compression waveform that isbecoming increasingly deeper (positive drift). However, the actualwaveform is more closely returning to the initial starting point, and isthus the drift shown in FIGS. 2 through 4 is considered a positivedrift. Likewise, arrows 24 and 25 in FIGS. 3 and 4 illustrate that drifthas an increasing affect on the reported velocity and the reportedacceleration. The effects of drift mean that the initial starting pointof compressions cannot be used as a reliable starting point for allcompressions. Accordingly, the starting point of compressions must bedetermined for every compression cycle. In addition, the other sourcesof noise must be either eliminated or greatly reduced.

FIG. 5 is a flow chart of a signal processing technique that converts araw acceleration into an estimated actual value for total compressiondepth. The raw acceleration 34 is filtered by a first filter in step 35to produce a filtered acceleration. The first filter comprises ahigh-pass filter and greatly reduces most forms of signal noise. (Inother embodiments the first filter may comprise a band pass filter, amoving average filter, an infinite impulse response filter, anautoregressive filter, or an autoregressive moving average filter.) Theeffects of the other steps shown in FIG. 5 are described in the contextof FIGS. 7 through 18.

FIG. 6 is a flow chart of an alternate signal processing technique thatconverts a raw compression acceleration into an estimated actualcompression depth. This flowchart is described after the description forFIGS. 7 through 18.

The effect of the filter operation 35 is seen in FIGS. 7 through 9,which show the graphs of compression depth, velocity, and accelerationover time for four hypothetical compressions after the first filteringstep 35. (FIGS. 7 through 9 show the output of the first filteringstep). The measured acceleration waveform 13 of FIG. 9 is much lessnoisy than the corresponding unfiltered waveform 13 of FIG. 4. Since thevelocity and depth waveforms of FIGS. 8 and 9 are derived from theacceleration waveform they, too, are less noisy. Nevertheless, theintegration process still causes the velocity waveform to be more noisythan the acceleration waveform and the depth waveform to be more noisythan the velocity waveform. In addition, the external acceleration spike22 still remains, as do the errors caused by drift (as shown by arrows24 and 25).

Returning to FIG. 5, the filtered acceleration is integrated in a firstintegration step 36 to derive the compression velocity. However, asshown in FIG. 8, without further processing the velocity waveform isstill noisy. Thus, the velocity is filtered by a second filter in step37 to produce a filtered velocity. The second filter comprises a highpass filter and further reduces most signal noise in the velocity anddepth waveforms. (In other embodiments the second filter may comprise aband pass filter, a moving average filter, an infinite impulse responsefilter, an autoregressive filter, or an autoregressive moving averagefilter.)

The effects of the filter operation 37 is seen in FIGS. 10 through 12,which show the graphs of compression depth, velocity, and accelerationover time for four hypothetical compressions after the second filteringstep 37. (FIGS. 10 through 12 show the output of the second filteringstep 37.) The measured velocity waveform 13 of FIG. 11 is less noisythan that of FIG. 8 (the velocity waveform after the first filteringstep). Since the depth waveform is derived from the velocity waveformit, too, is correspondingly less noisy. Nevertheless, the integrationprocess still causes the depth waveform to be slightly more noisy thanthe acceleration and velocity waveforms. In addition, the externalacceleration spike 22 still remains, as do the errors caused by drift(as shown by arrows 24 and 25).

Returning to FIG. 5, the filtered velocity is integrated in a secondintegration step 38 to calculate the chest compression depth. Signalnoise has been substantially eliminated and thus a third filtering stepis not required. However, the noise in the depth waveform, as shown inFIG. 10, is still slightly more than the noise in the velocity waveform,as shown in FIG. 11. Thus in other embodiments a third filter,comprising a high pass, bandpass, or other filter may be used to furtherreduce signal noise in the depth waveform.

After the initial filtering steps (35 and 37) and integration steps (36and 38), a baseline limiter estimates the actual starting point of acompression in step 39. The baseline limiter uses, among othertechniques described below, the starting points from past compressionsto estimate the current compression starting point. The baseline limiteritself comprises a digital or analog signal processor that operates onthe baseline portion of the compression depth waveform of FIG. 10. Thebaseline portion of the compression depth waveform comprises thatportion of depth waveform where the set of actual starting points ismost likely to be found. For example, the baseline may comprise theportion of the depth waveform that is equal to and below 1.1 inchescompression depth. (Larger changes in the starting points ofcompressions are unlikely, and signals indicating large changes areprobably wrong.) Thus, the limiter will disregard or arbitrarily assigna realistic depth value to any “starting point” above 1.1 inches depth.In one embodiment, past starting points above the baseline aredisregarded and a current starting point above the baseline is reportedor treated as an error. (Past starting points are the starting points ofcompressions that have already occurred. A current starting point is thestarting point of the current compression.) In another embodiment acurrent starting point above the baseline is assigned a smallprobability and averaged with the past starting points.

In one embodiment the baseline limiter estimates the starting point ofthe current compression by applying a moving average to all startingpoints that fall within the baseline portion of the depth waveform. Amoving average is a function that uses past data samples to modify thecurrent data sample. (Additional moving average techniques are describedbelow.) In the case of the baseline limiter, the baseline limiter mayweigh recent starting points more heavily than older starting points,meaning that the weight of a given starting point decays over time.Starting points that fall outside the baseline portion of the depthwaveform are given an arbitrary weight or no weight. By applying amoving average to all starting points the baseline limiter reduces theeffect of external acceleration error and drift on the current startingpoint. In other words, the moving average of all starting points will bestatistically closer to the current actual starting point than thecurrent measured starting point derived from the integration of theacceleration.

The following example shows an embodiment of a moving average technique.In this embodiment each compression starting point is given a weight of1.25% of the previous compression starting point. In other embodimentsthe weighting may comprise a percentage in the range of about 0.1% toabout 12.5% (which yields between about 0.3% to about 90% data weightingat the end of about 1 minute). In other words, the measured value of thecurrent starting point (starting point 1) is weighted 100%, the mostrecent starting point (starting point 2) is weighted 98.75%, the nextprevious starting point (starting point 3) is weighted 97.5%, the nextprevious starting point (starting point 4) is weighted 96.25%, etc untilall compressions are weighted. Eventually, compressions in the distantpast are given no virtually no weight at all. The depth of all theweighted starting points is then averaged. The weighted average of allstarting points is treated or reported as the current starting point.

In another embodiment, all compressions after a predetermined timeperiod (such as about 1 minute to about 15 minutes) are disregarded.Thus, only compressions within the last 1 to 15 minutes are averaged. Inanother embodiment, all compressions after a pre-determined number ofcompressions (such as about 5 to about 15) are disregarded.

Continuing the example, in one embodiment the measured values forstarting point 1=0.5 inches, starting point 2=1.1 inches, starting point3=4.0 inches, and starting point 4=0.9 inches. Starting point 3 isoutside the baseline portion of the depth waveform (the baseline portionis 1.1 inches and below in this example). Starting points outside thebaseline in this example are disregarded, so starting point 3 isdisregarded. Thus, the current starting point would be reported as:[(0.5*100%)+(1.1*98.75%)+(0.9*96.25%)]÷3=0.853 inches relative to theinitial starting point.

Had starting point 3 been included in the moving average, then thecurrent starting point would have been reported as:[(0.5*100%)+(1.1*98.75%)+(4.0*97.5%)+(0.9*96.25%)]÷4=1.615 inchesrelative to the initial starting point.Stated differently, this value is the estimated actual starting pointfor the current compression.

Mathematically, the reported value of the current starting point isexpressed as:Ds=[Σ(DB _(i)*ω^(i-1))]÷n _(r)

-   -   where DB_(i)=0 if DB_(i)>B,        where Ds is the depth of the current starting point, n_(r) is        the number of starting points remaining after all starting        points that exceed the baseline have been disregarded, i is the        starting point number (or sum index), DBi is the measured depth        of the i^(th) starting point, ω is the weighting constant, and B        is the baseline. Expressed differently, DB_(i)*ω^(i-1) is summed        from i=1 to n_(r) and the sum is divided by n_(r), but if a        particular DB_(i) is greater than B then that DB_(i) is instead        set to zero.

The baseline limiter may perform other functions to further increase theaccuracy and precision of the estimated depth of the current startingpoint. For example, a probability can be assigned to a given changebetween the current starting point and the immediate previous startingpoint. (Likewise a probability can be assigned to a given change betweenthe current starting point and the moving average of all previousstarting points.) Large changes in starting point may be given lessweight than smaller changes. This technique may be referred to as a“weighted moving average” technique.

Continuing the above example, measured depth 1 is treated as having a100% probability of occurring. Then, the difference between the currentstarting point (depth 1) and the previous starting point (depth 2) is1.1 inches−0.5 inches=0.6 inches. The probability of a step of 0.6inches occurring is assigned to be 97%, based on past experiments. Sincethe probability is not 100%, the current starting point is not treatedas having jumped a full 0.6 inches. Instead, the current starting pointis treated as having jumped 0.6*0.97=0.582 inches. Accordingly whencalculating the weighted moving average depth 2 is treated as being1.082 inches and not 1.1 inches. Starting point 3 is still disregarded.The difference between starting point 2 (1.1 inches) and starting point4 (0.9 inches) is 0.2 inches, which is assigned a 99% probability. Thus,the effective distance of the step between depth 2 and depth 4 is0.2*99%=0.198. Accordingly, depth 4 is treated as 0.902 inches insteadof 0.9 inches. Using the same moving average as above, the currentstarting point is now reported as:[(0.5*100%)+(1.082*98.75%)+(0.902*96.25%)]÷3=0.812 inches relative tothe initial starting point.Stated differently, this value is the estimated actual starting pointfor the current compression.

Mathematically, the reported value of the current starting point isexpressed as:Ds={Σ[DB _(i-j)+(DB _(i) −DB _(i-j))*P _(s)]*ω^(i-1) ]}+n _(r)

-   -   where DB_(i)=0 if DB_(i)>B,        where Ds is the depth of the current starting point, n_(r) is        the number of starting points remaining after all starting        points that exceed the baseline have been disregarded, i is the        starting point number, DBi is the measured depth of the i^(th)        starting point, j is the index for the most recent starting        point that was still within the baseline, DB_(i-j) is the most        recent starting point that was still within baseline, P_(s) is        the probability that a step of size DB_(i)−DB_(i-j) will occur,        ω is the weighting constant, and B is the baseline. The result,        Ds, is the reported depth of the current starting point.        Expressed differently,        [DB_(i-j)+(DB_(i)−DB_(i-j))*P_(s)]*ω^(i-1) is summed from i=1 to        n and the sum is divided by n_(r), but if a particular DB_(i) is        greater than B (the baseline) then that DB_(i) is instead set to        zero.

In another embodiment, a probability is assigned to the step sizebetween the depth of the current starting point and the weighted averageof all previous starting points. (In the above example, the probabilityis assigned to a step size between the current starting point and theimmediate past starting point). This technique may be referred to as a“weighted moving average with memory” technique. In this technique thereported depth of the current starting point is expressed mathematicallyas:Ds={Σ[DB _(i-j)+(DB _(i) −Ds _(i-j))*P _(s)]*ω^(i-1) ]}÷n _(r)

-   -   where Ds_(i-j)=[Σ(DB_(i-j)*ω^(i-2))]÷n_(r) and DB_(i)=0 if        DB_(i)>B,        where the variables are defined above. Again, the value for Ds        is also the estimated actual starting point for the current        compression.

In another embodiment, an autoregressive moving average (ARMA) filtermay be used as the baseline limiter. The ARMA filter is an exponentiallydecaying “forgetting” filter that weights more current data more heavilythan past data. The ARMA operates on more than just the compressionstarting point or peak values. Instead, the ARMA filter operates on datasamples of compression acceleration, velocity, or depth taken at rapidtime intervals. Data samples may be taken at a rate of about 100 samplesper second to about 2000 samples per second (with a rate of about 1000samples per second preferred). Thus, the ARMA filter operates on theentire waveform and not just on the compression peaks and the startingpoints.

In low pass form (which eliminates high frequency variations in thebaseline) the ARMA filter may be expressed mathematically as:y[n]=(1−α)*y[n−1]+α*x[n].

In this case, n is the index of the current sample (the “n^(th)”sample), y[n] is the output of the current sample, x[n] is the input ofthe current sample, y[n−1] is the output from the previous sample, and αis an independent term that determines how fast the filter “forgets”past outputs and the amount of influence the current input has on theoutput. The value for a may be in the range of about 0.02 to about0.0002, with a value of about 0.002 being suitable for many CPR-relatedfilter applications. Should it be desired to implement a high-pass ARMAfilter for the baseline limiter, then the ARMA equation becomes:y[n](high pass)=1−{(1−α)*y[n−1]+α*x[n]}where y[n](high pass) is the high pass filter output and the othervariables are defined in the context of the low pass ARMA filter. Thehigh pass filter may be used to eliminate low-frequency variations inthe depth, velocity, or acceleration signals.

The moving average techniques in the above examples have been describedin the context of processing the compression depth waveform. However,the techniques can be used to process the velocity waveform and theacceleration waveform, should it be desired to report accurate valuesfor the velocity and acceleration of compressions. The moving averagetechniques may be applied to each waveform separately. In other words,one does not necessarily apply a moving average technique to theacceleration waveform, then integrate the acceleration waveform, thenapply a second moving average technique to the velocity waveform, thenintegrate the velocity waveform, and finally apply a third movingaverage technique to the depth waveform. However, in other embodimentsthis procedure may be used.

Other methods for analyzing the baseline signal may be used to determinethe estimated actual starting point of compressions. Another embodimentof the baseline limiter comprises a signal processor that uses atransition probability map to identify the probability of particularshifts in the measured starting point. (The probability map may bepre-determined, such as by using a density estimator or kernelestimator, and then hard-coded into the compression monitor software.) Aparticular starting point measurement is compared to the probability mapand the system determines by how much a given shift in the measuredstarting point is erroneous. The reported starting point is adjustedaccordingly. (Likewise, a transition probability map may be used toestimate the actual peak and also the actual maximum depth for eachcompression.)

The effect of the baseline limiter 39 is seen in FIGS. 13 through 15,which show the graphs of compression depth, velocity, and accelerationover time for four hypothetical compressions. FIGS. 13 through 15 alsoshow the output of steps 35 through 39 in FIG. 5. The baseline limiterhas been applied separately to the velocity waveform (FIG. 14) in step47 and to the acceleration waveform (FIG. 15) in step 48.

FIGS. 13 through 15 show that a moving average technique reduces theeffect of drift in the reported starting point of each compression. (Themoving average techniques also reduce the effect of externalacceleration errors that appear in the baseline portion of thewaveform). Before correction, the reported starting points were becomingincreasingly deeper, though the actual starting points were returning toclose to the actual initial starting point. By applying a moving averagetechnique to the baseline of a measured waveform, the reported startingpoints of each compression are statistically closer to the actualstarting points. Accordingly, the compression monitor will report anestimated actual compression depth that is closer to the actualcompression depth. Arrows 49 and 50, which are shorter than arrows 24and 25 in FIGS. 2 through 4 and FIGS. 7 through 12, show the beneficialeffect of applying a moving average technique to each waveform.

Returning to FIG. 5, the compression depth waveform corrected by thebaseline limiter may be passed through a third filter in step 51 toreduce any accumulated signal noise in the compression depth waveform.The third filter comprises a high pass filter, though in otherembodiments the third filter may comprise a band pass filter.

Subsequently, the depth waveform (whether filtered or unfiltered) isprovided to a starting point detector in step 52. The starting pointdetector identifies the value of the current estimated starting point.The current estimated starting point is then provided to a means forcombining signals 53 (as indicated by line 54). The means for combiningsignals 53 will later use the current estimated starting point tocalculate the estimated actual compression depth. The means forcombining signals comprises a signal adder, a linear system model, anon-linear system model, or other means for combining signals.

Next, the compression waveform may be provided to a peak limiter in step55. The peak limiter is a signal processor that performs similarfunctions to the baseline limiter, but instead operates on the peakportion of a compression waveform. The peak portion of the waveformcomprises that portion of the waveform in which a peak is most likely tooccur. In one embodiment, the peak portion is the portion of thewaveform above the baseline portion. Continuing the example given forthe baseline limiter, the peak portion of the depth waveform would bethe portion of the depth waveform that is above 1.1 inches. The peaklimiter thus will smooth the peak portion of a waveform in much the sameway as the baseline limiter smoothes the baseline portion of a waveform.

In one embodiment the peak limiter sets an outside boundary on the sizeof the maximum compression depth. Thus, the peak limiter eitherdisregards (throws out) or sets an arbitrary value to any peak that isgreater than a known, improbable peak value (the depth of a largeperson's chest, for example, would not be a probable value for CPRcompression depth). Thus, the peak limiter prevents the compressionmonitor from reporting a compression depth that is improbable.

The effect of the peak limiter is seen in FIGS. 16 through 18, whichshow the graphs of compression depth, velocity, and acceleration overtime for four hypothetical compressions after the peak limiter step 55in FIG. 5. (FIGS. 16 through 18 show the output of steps 35 through 55).A peak limiter has been applied separately to the velocity waveform instep 56 and to the acceleration waveform in step 57. By applying amoving average technique to the peak portion of the compressionwaveforms, the effect of the external acceleration spike 22 has beengreatly reduced. Combined with the techniques discussed in the previousprocessing steps, the reported waveforms are now close to the actualwaveforms.

Returning to FIG. 5, the estimated peak may optionally be provided to afourth filter 58 to remove remaining signal noise. The fourth filtercomprises a high pass filter, though in other embodiments the fourthfilter may comprise a band pass or other filter.

Subsequently, the depth waveform is provided to a peak detector in step59. The peak detector identifies the value of the estimated peak (theestimated maximum depth of the current compression). The estimated peakis then provided to the means for combining signals 53. The means forcombining signals 53 combines the estimated starting point 52 with theestimated peak 59 to produce an estimated actual compression depth forthe current compression 61. The estimated actual depth is then providedto a means for user feedback 62 (a user feedback system). The means foruser feedback may comprise a speaker, a visual display, one or moreLEDs, a vibrator, radio, or other means for communicating with therescuer. The user feedback system in turn provides informationcorresponding to the estimated actual depth of the current compressionto the rescuer.

In the technique of FIG. 5, the baseline portion and the peak portion donot overlap. Thus, the compression depth waveform may be thought of ascomprising two portions, the baseline portion and the peak portion. Eachportion of the depth waveform is treated differently by two differentprocedures (the baseline limiter and the peak limiter) to extractdifferent information. Thus, both the baseline limiter and the peaklimiter operate on the same depth waveform. The effect of this is thatthe signal comprising the depth waveform is provided first to thebaseline limiter and then to the peak limiter (the signal is not split).

The technique shown in FIG. 6 may be used when the baseline portion andthe peak portion overlap (though the technique may also be used when thebaseline portion and peak portion do not overlap). For example, thetechnique of FIG. 6 may be used when the baseline portion is set below1.5 inches (relative to the chest's relaxed position) and the peakportion is set above 1.0 inches (relative to the chest's relaxedposition). In this case the signal representing the depth waveform issplit and is provided to two separate processors, a baseline limiter anda peak limiter. Each processor performs similar functions to thelimiters already described. Thus, although the baseline limiter and thepeak limiter act independently of each other, the technique of FIG. 6produces an estimated starting point and an estimated peak in much thesame was as the technique shown in FIG. 5. The means for combiningsignals then combines the estimated starting point and estimated peak instep 53 to produce the estimated actual depth of the currentcompression. The estimated actual depth of the current compression isprovided to the user feedback system in step 62. The user feedbacksystem in turn provides the estimated actual depth of the currentcompression to the rescuer.

In addition to the signal processing techniques of FIGS. 5 and 6, othertechniques can be used to correct for errors in the compression depthwaveform. For example, FIG. 19 is a flow chart of a signal processingtechnique that uses a change in ECG noise 63 to activate a switch 64that, in turn, controls when an accelerometer begins to measureacceleration.

To implement this technique, the compression monitor is provided withone or more electrodes, or some other means for measuring the patient'sECG. As the rescuer performs compressions the patient's ECG becomesnoisy. Even if the patient's actual ECG is flat (shows no activity) thereported ECG will still show the noise caused by chest compressions.Indeed, a motion artifact signal (an ECG noise component caused by chestcompressions) will be superimposed on any ECG rhythm. Whatever theactual ECG rhythm, the ECG noise may be isolated and accounted for.

Since the bulk of ECG noise during compressions is caused by the act ofcompressing the chest, the starting point of a compression may becorrelated to the point where the ECG noise exceeds a pre-determinedthreshold. However, there is some delay or lag between the onset of acompression and the onset of ECG noise. The time lag is on the order ofmilliseconds to tenths of a second. In order not to miss any part of acompression, a buffer (either digital or analog) may be employed tocorrect for the time lag. Thereafter, when the ECG noise exceeds theparticular threshold then the switch is programmed to activate theaccelerometer (which will begin to take acceleration measurements).Total compression depth is then determined by double integrating themeasured acceleration.

The effect of using ECG noise as a reference sensor to establish thestarting points of compressions is seen in FIGS. 20 through 25, whichshow compression depth, velocity, and acceleration over time for fourhypothetical compressions. No signal processing is applied to any ofwaveforms shown in FIGS. 20 through 22. The phantom waveforms 12represent the actual waveforms for compression depth, velocity, andacceleration (measured independently of the accelerometer). The solidwaveforms 13 represent the waveforms derived from the accelerationmeasured by the accelerometer. The solid waveforms are also thewaveforms reported by the compression monitor. The effects of signalnoise are shown by the rough nature of the solid waveforms. The effectsof external acceleration noise are shown by the two spikes, 65 and 66,in the reported waveform. The effects of negative drift (increasinglyshallow compressions) are shown by the increasing distance (representedby arrows 67 and 68) between the minimums in the reported and the actualwaveforms.

The effect of using ECG noise as a reference sensor to establish thestarting points of compressions is seen in FIGS. 23 through 25, whichshow graphs of compression depth, velocity, and acceleration over timefor hypothetical compressions. Using ECG noise as a reference sensorreduces certain external acceleration errors and reduces the effect ofnegative drift. (The ECG noise reference sensor can also reduce theeffect of positive drift). Specifically, the ECG noise reference sensorreduces the effect of external acceleration noise that occurs near acompression minimum. Since the accelerometer is not “on,” a portion ofthe external acceleration spike is “ignored”. In practice theaccelerometer is still taking data, but software or hardware is used toprocess out accelerometer data or signals that occur during a timeperiod where ECG noise does not reach a predetermined level. In othermethods, the estimated actual depth of compressions is calculated whenthe ECG noise falls within a predetermined threshold. In any case, theeffect of spike 65 is reduced in the reported waveform. However, theaccelerometer by itself still cannot tell the difference between acompression-related acceleration and an external acceleration. Thus, thereported waveform is still subject to external acceleration noise thatoccurs during a compression, as shown by spike 66.

Nevertheless, the ECG noise reference sensor does reduce the effects ofdrift. Since the starting point of a compression is independentlyestablished, the waveform is much less subject to either positive ornegative drift. In other words, the accelerometer will always measureacceleration after the actual start of compressions. Thus, the reportedwaveform of FIG. 23 more accurately shows what the rescuer is actuallydoing—compressing the chest from starting points that are becomingincreasingly deep. Thus, peaks 69 and 70 show that the measured waveformmore closely matches the actual waveform.

Although the ECG noise reference sensor can reduce the effects of driftand reduce the effect of some forms of external acceleration noise,signal noise remains a problem. Thus, FIGS. 23 through 25 still show thesame levels of signal noise as shown in FIGS. 20 through 22. To reduceall forms of noise the ECG noise reference sensor may be combined withthe signal processing techniques of FIG. 5 or 6. The combined techniqueswill produce a reported depth waveform that is close to the actualwaveform.

Other reference sensors may be used to establish the actual startingpoint of a compression. FIG. 26 shows an accelerometer-based compressionmonitor in place on a patient 1 who is lying on a surface 80. A systemof reference sensors comprising an accelerometer 81, a load sensor 82,and a switch 83 are disposed such that each sensor may measure variousparameters related to chest compressions. In the case of referenceaccelerometers, the reference accelerometers may be disposed elsewhereon the patient, or upon any reference object that experiences the sameexternal accelerations the patient experiences. The referenceaccelerometers may comprise a three-axis accelerometer, but may alsocomprise three orthogonal single-axis accelerometers or one single axisaccelerometer (in which case the accelerations along the other two axesare assumed to be negligible).

The reference accelerometers 81 allow a signal processor to eliminateexternal acceleration error, such as those accelerations caused bytransporting the patient. In one method, the acceleration sensed by thecompression monitor or automatic CPR device (the device acceleration) isprovided to a signal processor. The device acceleration contains theacceleration caused by compressions (the compression acceleration) andthe acceleration caused by the external accelerations (the externalacceleration). Next, the reference accelerometer or accelerometersprovide a reference acceleration to the signal processor. The referenceacceleration contains only the external acceleration of the patient.Then the reference acceleration is combined with the device accelerationto produce an estimated actual acceleration. (The effect of compressionaccelerations on the reference acceleration is negligible since thesurface and patient are kept steady with respect to the compressionmonitor.)

Once obtained, the estimated actual acceleration may be doubleintegrated to produce an estimated actual chest depth. Thus, the depthof compressions may be determined even in the presence of large externalaccelerations. Moreover, the position signal may be made more accurateand precise by combining the actual acceleration with the signalprocessing technique of FIG. 5 or 6, or with other signal processingtechniques.

In lieu of (or in addition to) the ECG noise sensor and referenceaccelerometers, other reference sensors may be used to set the actualstarting point of a compression. Reference sensors may comprise a loadsensor 82, a switch 83, a transthoracic impedance detector, an ECG noisedetector (as described above), a voltage or current sensor in anautomatic CPR device, a start signal in an automatic CPR device, anencoder in an automatic CPR device, or any other sensor capable ofindependently detecting the actual beginning of a compression. When thereference sensor detects the beginning of a compression then thestarting point is set to zero. The acceleration is then processed toderive compression depth. The technique of setting the starting point tozero when a reference sensor detects the beginning of a compression mayalso be combined with the signal processing techniques of FIG. 5 or 6.

In the case of a switch 83, the switch is disposed such that when acompression begins the switch will be closed. For example, the switchmay be disposed beneath or on the compression monitor, on the patient 1,on the surface 80 upon which the patient lies, on the rescuer's hand, ona CPR machine, on the patient, or on some other location that allows theswitch to register that a compression has begun.

The switch may comprise many different types of switches and sensors,including a contact switch, a motion sensor, a voltage sensor on anautomatic CPR device, an optical, rotary, or other encoder on anautomatic CPR device, the displacement of a shaft or other component onan automatic CPR device, a potentiometer, a strain gage, apiezoresistive transducer, a differential transformer, synchro andinduction potentiometers, variable-inductance and variable-reluctancepickups, an eddy current non-conducting transducer, a capacitivetransducer, an electro-optical transducer, a photographic switch, avideo tape switch, a holographic switch, a switch that uses photoelastictechniques, translation encoders, an ultrasonic transducer, moving coiland moving magnet pickups, an AC or DC tachometer, an eddy-currentdrag-cup tachometer, additional accelerometers, or a gyroscopicdisplacement switch.

In the case of the load sensor 82, the load sensor may be operativelyconnected to the rescuer, the patient, an automatic CPR device, beneaththe patient, or elsewhere so long as the load sensor senses a load whencompressions begin. When the load sensor measures a load that exceeds apre-determined threshold, then the measured starting point is set tozero. The load sensor may also be operatively connected to a switch,which activates when the load sensor senses a load, or the load sensormay merely provide input to a signal processor system identifier(described in more detail below). Compression depth is then determinedby integrating the acceleration twice. The technique of setting thestarting point to zero when a load sensor detects the beginning of acompression may also be combined with the signal processing techniquesof FIG. 5 or 6.

In another embodiment of the load sensor 82, the load sensor may bedisposed such that the sensor can sense both the weight of the patientand the force of compressions. The load sensor 82 may be disposedbeneath the surface 80 upon which the patient 1 rests. Duringcompressions the force of pressing on the patient causes the load sensorto report a total force greater than the patient's weight. Accordingly,a starting point is set to zero when the total force is about equal tothe patient's weight.

Examples of force sensors that can be used with this technique includepressure sensors, elastic force transducers, shaft displacement on anautomatic CPR device, a voltage or a current sensor on an automatic CPRdevice, an optical, rotary, or other encoder on an automatic CPR device,bonded strain gages, beam strain gages, differential transformers,piezoelectric transducers, variable reluctance/FM oscillators,gyroscopic force transducers, and vibrating wire force detectors.Examples of pressure sensors that can be used with this techniqueinclude deadweight gages, manometers, elastic transducers, piezoelectrictransducers, and force-balance transducers.

In the case of a transthoracic impedance detector, one or more ECG,defibrillation, or other electrodes are disposed on the patient'sthorax. When a compression begins the impedance of the thorax changes.The thoracic impedance comprises the impedance due to skin and thoraciccontents between any two electrodes. The change in thoracic impedancemay be measured by a small test current or by any other means formeasuring impedance. When the impedance changes by a pre-determinedamount then the starting point is set to zero. Total compression depthmay then be determined by processing the measured acceleration.

Because the compression monitor can measure the compression waveform,the compression monitor can also prompt the rescuer or an automatic CPRdevice to perform a particular compression waveform. FIG. 27 shows acompression waveform that the compression monitor may prompt the rescuerto perform. Depth is measured in inches and time is measured in seconds.The scale shown in FIG. 27 is marked in 0.5 second intervals and 1.0inch intervals respectively. The compression phase of the cycle isindicated by the positively sloped curve 84. The compression phase ofthe cycle ends at the maximum compression depth 85 (compression peak).The decompression phase of the cycle is indicated by the negativelysloped curve 86. The decompression phase ends when the rescuer begins anew compression at the next starting point 87 (or baseline), which mayor may not be at the initial starting point. Compressions are initiatedat time=0 and depth=0, and the total depth of compressions is thedistance represented by arrows 88.

The compression waveform includes a compression hold 89, where therescuer maintains a hold at maximum compression depth for a short periodof time, and an incomplete decompression hold 90, where the rescuermaintains a short hold at a point deeper than the initial startingpoint. Each compression and decompression is performed quickly, at highacceleration and velocity, as indicated by the relatively steep slopesof the compression phase 84 and the decompression phase 86. The dutycycle is slightly less than 50% (the ratio of compression anddecompression time is 1), meaning that slightly less time is spent inthe compression phase, as indicated by the distance between arrows 89,than in the decompression phase, as indicated by the distance betweenarrows 90.

Although the compression waveform of FIG. 27 shows an example of aparticular waveform that the compression monitor can instruct a rescuerto perform, other waveforms are also possible. For example, anotherwaveform may lack a compression hold phase. Yet another varies the dutycycle and others increase the compression hold time. The exact waveformdepends on the current state of the art of what kind of compressionwaveform comprises an optimal compression waveform for a particular kindof patient. In addition, the compression monitor may be provided with aswitch, button, software, or other means for user input which allows therescuer to enter the size or shape of the patient. The compressionmonitor may use this information to choose a particular waveform from alibrary of waveforms. The compression waveforms are thus adaptable tofindings in future research, AHA guidelines, rescuer observations, andmedical professional preferences. Accordingly, at various timesdifferent waveforms may be provided to the user feedback system, asdescribed more fully below.

The prompted waveform may be provided by the user feedback system (step62 in FIG. 5). In addition, the user feedback system may provide therescuer or automatic CPR device with other compression-relatedinformation. For example, the user feedback system may displayinformation regarding the starting point of compressions, thecompression depth waveform, the compression velocity waveform, and thecompression acceleration waveform. Thus, the user feedback system mayprovide the rescuer or the automatic CPR device with all of the dataneeded to continuously track the position, velocity, and acceleration ofthe chest during all phases of CPR. This information may be used toevaluate the performance of a rescuer or automatic CPR device.

The user feedback system may also provide a rescuer or automatic CPRdevice with information concerning the compression phase quality and thedecompression phase quality. Compression phase quality is the quality ofcompressions with respect to total compression depth, the duty cycle,the acceleration of compressions, smoothness of compressions, and otherfactors related to the compression phase. Decompression phase quality isthe quality of compressions with respect to whether the rescuer returnsto the actual initial position, the duty cycle, the acceleration ofdecompressions, the smoothness of decompressions, and other factorsrelated to the decompression phase. The rescuer or automatic CPR devicemay use this information to evaluate or prompt the kind and quality ofcompressions.

The user feedback system 62 may provide the rescuer or automatic CPRdevice with information concerning compression phase quality bycombining information gained from the acceleration, velocity, andposition waveforms. For example, the user feedback system can instructthe rescuer to increase compression force when the depth of compressionsare less than recommended guidelines and to reduce compression forcewhen the depth of compressions are greater than recommended guidelines.The user feedback system may also instruct the rescuer with regard toother compression phase parameters of a compression waveform. Forexample, the user feedback system can inform the rescuer or automaticCPR device if the time to achieve proper compression depth is too shortor too long.

The user feedback system 62 may also provide the rescuer or automaticCPR device with information regarding decompression phase quality bycombining information gained from the acceleration, velocity, andposition waveforms. For example, the user feedback system can instructthe user or device on the proper position at which to rest after adecompression. Thus, the feedback system can instruct the user or deviceto allow the chest to fully relax if the rescuer or device is notallowing the chest to fully return to its initial starting position.Conversely, should it be medically indicated, the user feedback systemcan instruct the user or the device to return to a depth just below theinitial chest position. In this case, the rescuer or device implements a“decompression hold” and maintains force on the chest even when thecompression cycle reaches its minimum depth. In another case thefeedback system can indicate different compression starting points atdifferent times. Thus, the user feedback system can instruct the rescueror device to apply incomplete decompression holds during compressioncycles, but to allow the chest to return to its fully relaxed positionduring ventilation pauses. The user feedback system may also instructthe rescuer or device with regard to other decompression phaseparameters, such as the decompression rate and the duty cycle of thedecompression phase.

Taken together, the information gained from the compression phasequality and the decompression phase quality enable the user feedbacksystem to prompt the rescuer on how to perform an optimum compressionwaveform and an optimum compression duty cycle. A rescuer performs aparticular compression waveform by performing compressions at apre-determined depth and rate, and by holding the chest at apre-determined compression depth for a pre-determined time. A rescuerperforms a particular duty cycle by compressing the chest for apre-determined period and allowing the chest to relax for anotherpre-determined period.

Thus, the user feedback system can prompt the rescuer or automatic CPRdevice to perform at the appropriate compression rate, compressiondepth, compression velocity (the time required to compress or decompressthe patient), compression acceleration, and compression hold time foreach phase (compression and decompression) of the compression cycle.Accordingly, the compression waveform that the rescuer or deviceactually applies can conform to a complex compression waveform. Sinceresearch has shown that most patients benefit from more complexwaveforms, patient survival is likely to increase if the rescuer orautomatic CPR device uses a compression monitor with this user feedbacksystem.

Similarly, the user feedback system 62 of FIG. 5 can provide the rescueror CPR device with feedback regarding the compression duty cycle. Theduty cycle is the ratio of time under compression to the time underdecompression for each compression cycle. (However, the duty cycle doesnot include time periods where no compressions are taking place, such asduring ventilation.) If the duty cycle does not fall withinpre-determined parameters, then the user feedback system may prompt therescuer to adjust compression timing and compression rate in order toeffect an optimal duty cycle.

The user feedback system described above comprises the last step in aparticular solution to the problem of determining an accurate value forchest displacement from a raw acceleration signal. (FIG. 5 is theflowchart for this solution). Many variations of that solution exist, asalready described, though it is possible to view the problem from ageneral perspective and to create a general solution.

FIGS. 28 and 29 are block diagrams that represent the general problem toand the general solution for determining an accurate position from anacceleration measured during CPR. FIG. 28 is a block diagram of how anactual chest compression acceleration is converted into a corruptedvalue for chest position. In broad terms, the actual acceleration 105,signal noise 106, external acceleration noise 107, and some forms ofdrift 108 are combined by an unknown function 109 (which may be linearor non-linear and may include random or deterministic inputs). Theunknown non-linear function is known as the system, which produces thecorrupted acceleration 110 measured by the accelerometer. The corruptedacceleration is then integrated twice, which greatly compounds theproblem introduced by the corruption in the acceleration. The increasederror is referred to as integration error 111 (although it is assumedthat the integration technique itself does not directly contributeerrors into the position). Finally, additional sources of drift 112 canaffect the final value for the corrupted position 113.

FIG. 29 is a block diagram of a general solution for converting acorrupted chest compression acceleration into an estimated actual depthof chest compressions. First, a reference sensor 119 may establish theactual starting point of a compression. Thus, the starting point of theacceleration will be known. (Although helpful, the reference sensor 119is not necessary to the general solution). The actual acceleration 105and the real or the estimated noise sources 120 (which comprise blocks106 through 108 of FIG. 28) are combined by the system 109 by an unknownfunction. The result is the corrupted acceleration 110. The measuredacceleration is then provided to a means for combining data 121 (whichmay comprise a linear or a non-linear function) and to a systemidentifier 122.

The system identifier comprises one or more functions (either linear ornon-linear) that model the system. One or more noise references that canbe correlated to the noise sources 120 may also be provided to thesystem identification function 122. For example, noise identified by alow frequency filter can be correlated to signal noise or a referenceaccelerometer can be correlated to the external acceleration noise.

The system identification function may also use various parameters of anautomatic CPR device as noise source references, even if the referenceitself does not produce noise in the acceleration. However, the noisesource reference must somehow be correlated to a source of noise in theacceleration signal. For example, the accelerometer-based depthmeasurement reports a chest depth of 0.5 inches. However, a simultaneouscurrent spike in the automatic CPR device informs the system that theCPR device is compressing the chest much harder than should be requiredto achieve a chest depth of 0.5 inches. The discrepancy may be caused byexternal acceleration noise or by drift. Thus, the current spike may becorrelated to a source of noise in the system. This information may beused by the system identifier to help model the system. Likewise,voltage, shaft displacement, or optical or rotary encoders may be usedas references by the system identifier to help model the system. (Again,the noise references are useful but not necessary).

The system identifier then combines or correlates the noise sourcereferences and the measured acceleration in order to produce theestimated noise 123 in the measured acceleration. The estimated noise123 is then provided to the means for combining data 121. The means forcombining data combines the estimated noise 123 and the measuredacceleration 110 to produce an estimated actual acceleration 124. Theestimated actual acceleration is then integrated 125 twice. Filters 126may optionally be used during one or both integration steps to reducethe compounding effect of errors that may still linger in the estimatedactual acceleration. The final result is an accurate and preciseestimate of the actual position 127 of the accelerometer.

The system identification function 122 models the system and thus can beused to estimate the noise in the acceleration. (Once the noise is knownit can be easily eliminated by combining the noisy acceleration with themeasured acceleration.) In other words, system identification is theprocess of using the input and output data to model the function thatcombines the actual acceleration and the sources of noise in theacceleration. The system identification problem has a known or measuredoutput and an input that may be known or unknown. The addition of knownor measured input is beneficial to system identification, but notnecessary. The system itself is an unknown arbitrary function that canbe linear or non-linear, though some boundary conditions may be known.

A number of methods, both linear and non-linear, may be used to modelthe system. Each of these methods may comprise, alone or in combination,the system identification function in step 122. These methods mayoperate by taking many data samples per second, as opposed to operatingonly on the compression starting points or peak points. Nevertheless,these methods may also be performed on the compression starting pointsor the compression peaks. A partial list of these methods include:autoregression, autoregression with extra inputs, autoregressive movingaverage (which is one of the methods used in the techniques shown inFIGS. 5 and 6) autoregressive moving average with extra inputs,autoregressive integrated moving average, autoregressive integratedmoving average with extra inputs, a Box-Jenkins model, an output errormodel, a hidden Markov model, a Fourier transform, a wavelet transform,wavelet de-noising, wavelet filtering, adaptive neural networks,recurrent neural networks, radial basis function nets, adaptive curvefitting (splines), Kalman filters, extended Kalman filters, adaptiveKalman filters, unscented Kalman filters, and kernel estimation.Algorithmic approaches that may be used to find the systemidentification function include maximum entropy, maximum likelihood,recursive least squares (or similar techniques), numerical methods,unconstrained global search or optimization, expectation minimization,and fast Fourier transforms.

In the case of recursive identification, the formula for generalrecursive identification may be expressed as:X(t)=H(t,X(t−1),y(t),u(t)) and  (1)θ(t)=h(X(t)),  (2)where X(t) is the state of the system at time t; H is the state of thetransfer function; X(t−1) is the past system state; Y(t) is the measuredoutput; u(t) is the measured input; θ(t) is the system, and h transformsthe system state to the output. The system state can be converted to thesystem output by h(X(t)).

Since X(t) and θ(t) are evaluated at each time point as u(t) and Y(t)are collected, the total amount of previously collected data has a muchmore profound effect on the system than do the most recently collecteddata.

Equations (1) and (2) may be simplified into equations (3) and (4):X(t)=X(t−1)+μQ _(x)(X(t−1),y(t),u(t)) and  (3)θ(t)=θ(t−1)+γQ _(θ)(X(t−1),y(t),u(t)),  (4)where μ and γ are small numbers that reflect the relative amount ofinformation provided by the latest time step. Q is a function thatrelates inputs, outputs, and states. Equations (3) and (4) are moresimple in the sense that it is more simple to compute the equations byrecursive than equations (1) and (2).

A number of numerical algorithms may be used to solve equations (3) and(4). A partial list of numerical algorithms include recursive leastsquares, recursive (or recursion) instrumental variables, recursiveprediction error methods, recursive pseudolinear regression, recursiveKalman filters (including time varying parameters), and recursive Kalmanfilters for time varying systems. These numerical techniques encompassmany of the famous “named” techniques as special cases, including aKalman filter, an extended Kalman filter, extended recursive leastsquares, and others. Each algorithm has strengths and weaknesses, butall asymptotically approach a solution to equations (3) and (4).

The “named” special cases may be derived from general equations (3) and(4) when certain conditions or assumptions are made. Thus, the equationsfor each of the listed algorithms can be further specified. For example,when using a recursive least squares algorithm equation (4) may beexpressed as:θ(t)=θ(t−1)+L(t)[y(t)−φ^(T)(t)θ(t−1)] where  (5)

$\begin{matrix}{{L(t)} = {\frac{{P\left( {t - 1} \right)}{\phi(t)}}{{\lambda(t)} + {{\phi^{T}(t)}{P\left( {t - 1} \right)}{\phi(t)}}}\mspace{14mu}{and}}} & \left( {5a} \right)\end{matrix}$

$\begin{matrix}{{P(t)} = {{P\left( {t - 1} \right)} - {{P\left( {t - 1} \right)}{\phi(t)}{\phi^{T}(t)}{{P\left( {t - 1} \right)}.}}}} & \left( {5b} \right)\end{matrix}$

In equations 5 through 5(b) L(t), P(t), and P(t−1) are terms used tosimplify the equation, φ(t) is a regression vector, λ(t) is a forgettingfactor (described in more detail below), and φ^(T)(t) is the transposeof the regression vector.

In addition, equation 4 can also be expressed for the cases of recursiveinstrumental variables, recursive prediction-error methods, recursivepseudolinear regression, a recursive Kalman filter for time-varyingsystems, and a recursive Kalman filter with parametric variation.

Once the system identification algorithm as been selected from the aboveset of algorithms, there are several additional parameters that mayaffect the quality of the model. These additional parameters includedata weighting, choice of updating step, choice of updating gain, andmodel order selection. In the case of data weighting, when a system istime-varying the input-output data near the present time more accuratelyreflects the nature of the present system. Data recorded further back intime is more closely related to a past system state. To reflect thisfact the data can be weighted to favor a more recent system state.Actual data weighting is accomplished by the “forgetting factor,” λ, inequations 5 through 5b. The selection of λ is made based on informationabout how fast the system changes state. A typical range for λ isbetween about 0.9800 and about 0.9999 (though λ may be 1.0000 if no“forgetting factor” is desired).

Another way of thinking about the effect of λ on the system identifieris to evaluate at what point a data sample is given a weight of about36%. (36% is the value of the number e⁻¹, which is the value at which adata sample may be considered statistically insignificant). At thisweight a data sample's statistical significance becomes relativelysmall. The sample number at which a data sample has a weight of about36%, known as T₀, may be mathematically expressed as:

$T_{0} = {\frac{1}{1 - \lambda}.}$T₀ (and hence λ) is selected with appropriate knowledge of the systemstate and can be used to tune system identification so that theestimated actual acceleration most closely approximates the actualacceleration if the actual acceleration were independently measured.Thus, T₀ is pre-set before the compression monitor begins takingmeasurements.

The closer λ is to 1 the more samples are needed to reach the pointwhere a given data sample is given a weight of about 36%. A smaller λmeans that a given data sample is “forgotten” more quickly. For example,if λ is equal to 0.9800 then T₀=50 samples, meaning that the 50^(th)sample receives a weight of about 36%. However, if λ=0.9999 thenT₀=10,000, meaning that sample number 10,000 receives a weight of 36%.In the upper limit, if λ=1 then T₀=∞, meaning that a data sample isnever “forgotten” (it always receives a weight of 100%).

The sampling rate (how many times a second the acceleration is measured)affects the how λ changes the system identifier. If samples are taken1000 times per second then data may be “forgotten” rapidly on the timescale of CPR compressions. For example, if the sample rate is 1000 timesper second and T₀=1000 then data from just 1 second in the past is givena weight of 36%. In practice, the sampling rate may vary from about 100samples per second to about 2000 samples per second. A useful samplerate for signal processing acceleration measurements during CPR is about500 samples per second. In other embodiments the sample rate may befaster, but every certain number of samples may be ignored. For example,samples may be taken at 1000 samples per second, but every other sampleignored. This process, known as decimation, has the same effect as aslower sample rate.

In the preceding discussion the forgetting factor was a fixed number; itdid not change with time. However, λ can vary with time so that thesystem identifier may adapt to changing situations. For example, λ mayvary during a ventilation pause and in one embodiment λ increases duringa ventilation pause. The effect of increasing λ during ventilationpauses is to discard data points very quickly. Thus, the compressionmonitor will not report a change in compression depth during aventilation pause.

In addition to adding a forgetting factor to the system identificationfunction, the choice of updating step affects the quality of the model.(Although only some of the system identification techniques require anupdate; for example, a Kalman filter requires an updating step). Theupdate step can be implemented using a variety of methods. Some systemidentifiers may be solved analytically, such as the Kalman filter, andthe updating step may be solved analytically. Other system identifiersmust be solved numerically. Three updating methods that may be used whena numerical solution is required are a Gauss-Newton update, a gradientupdate, and a Levenberg-Marquardt update. The Gauss-Newton updateconverges to an accurate fit of the actual solution, though it requiresa large number of steps (and thus more computation time). The gradientupdate converges quickly but does not converge as accurately to theactual solution as the Gauss-Newton update. The methods may be combined.The gradient update is used first to converge the fit quickly and thenthe identifier switches to the Gauss-Newton update to achieve the finalfit. This combined technique is known as a Levenberg-Marquardt update.

Mathematically the Gauss-Newton update may be expressed as:R(t)=R(t=1)+γ(t)[φ(t)φ^(T)(t)−R(t−1)].Mathematically the gradient update may be expressed as:R(t)=I|φ(t)|² =R((t−a))+γ(t)[I|φ(t)|² −R(t−1)].In both equations R(t) is the Hessian of the identification criterion,R(t−1) is the Hessian of the identification criterion in the previoustime step, γ(t) is the updating gain (which is related to the forgettingfactor), and φ(t) is the regression vector.

The choice of updating gain is another step that is used in manyrecursive system identification functions. The choice of updating gainmay be expressed mathematically as:γ(t)=(1+λ(t)/γ(t−1))⁻¹.Thus, the updating gain is related to the forgetting factor.

With regard to model order selection, the recursive systemidentification techniques fit a system model to input-output data. Thestructure of that model must be determined before the recursive. Thestandard way of solving the model structure problem is to solve a widerange of model structures and then select which model fits the databest. A simple measure of model fit, like the mean squared error, tendsto over-estimate the model order and fit to the process or measurementnoise. If a model is under-estimated, critical components of the systemmight be missed. Several measures of model fit, or metrics, areevaluated over a range of model orders. The model order is the number ofterms used in the model. The smallest model order that minimizes the fitis the appropriate model.

Several techniques can be used to estimate smallest model order,including final prediction error (FPE), Akaike's information criterion(AIC), maximum description length (a variant of the AIC), andstatistical hypothesis testing (such as the student's t-test). The finalprediction error can be expressed mathematically as:

$\begin{matrix}{{{FPE} = \frac{V\left( {1 + \frac{d}{N}} \right)}{\left( {1 - \frac{d}{N}} \right)}},} & (11)\end{matrix}$where V is the quadratic loss function, d is the size of the modelorder, and N is the number of data points.

Akaike's information criterion may be expressed as:AIC=log [V(1+2(d/N))],  (12)where V is the quadratic loss function. The quadratic loss function maybe any quadratic function that relates the additional cost function ofusing additional terms.

The system identification techniques described above have been describedin the context of solving the problem of estimating actual compressiondepth from a raw acceleration measurement. These techniques may also beused to process a noisy ECG signal. FIG. 30 is a block diagramillustrating the problem of ECG noise caused by CPR and other sources ofnoise. Stated differently, FIG. 30 is a block diagram of how atheoretical actual ECG signal 135 and the noise sources are combined toproduce the measured ECG (which contains the motion corruptingartifact). The ECG noise, comprising ECG noise due to compressions 136and other sources of noise 137, are combined with the actual ECG by anunknown linear or non-linear function known as the system 138. Theprimary source of noise in the ECG is due to the motion of compressions,though other sources of noise exist and may be accounted for by thesolution presented below. The system produces a corrupted ECG 139 which,if left unprocessed, cannot be used to accurately report the electricalactivity of the patient's heart. In addition, the system combines theECG noise and the actual ECG in a way that causes the ECG noise tooverlap the actual ECG in the frequency domain. Thus, a simple bandpassfilter is insufficient to accurately process the corrupted ECG. (Asimple bandpass filter will eliminate important components of the actualECG as well as eliminating the ECG noise).

FIG. 31 is a block diagram of a general solution to the problemillustrated in FIG. 30 and illustrates the process of converting amotion corrupted ECG signal into an estimated actual ECG signal. As withFIG. 30, the system 138 combines the actual ECG 135 and the ECG noise136 to produce the corrupted ECG 139 that is measured by an observer.Next, the measured ECG 139 and a reference corresponding to the ECGnoise 136 are provided to a system identifier 140. For example, sinceCPR induced motion is the largest cause of ECG noise, a signalcorresponding to CPR induced motion may be provided to the systemidentifier. Specifically, a force transducer may be disposed on acompression monitor (or on the patient or rescuer) such that the forcetransducer measures force during a compression. A signal correspondingto the force is provided to the system identifier as a reference signal.Other signals corresponding to CPR induced motion may comprise variousparameters of an automatic CPR device. For example, a signal correlatingto the displacement of a drive shaft or other component can becorrelated to the CPR motion, a signal corresponding to the change incurrent or voltage required to drive the device can be correlated to theCPR motion, or signals produced by optical or rotary encoders may becorrelated to the CPR motion.

The system identifier models the system and then estimates the noisecomponent of the measured ECG signal (the estimated noise 141). Theestimated ECG noise 141 and the measured ECG 139 are then provided to ameans for combining signals 142, which combines the ECG noise and themeasured ECG to produce the estimated actual ECG 143. Since theestimated actual ECG is produced during compressions, the signalprocessing method allows the ECG sensor to detect the heart's normalsinus rhythm even during compressions. Thus, there is no need toperiodically pause compressions to check for the existence of a pulse.As a result, the overall quality of CPR increases and the patient ismore likely to survive.

The system identifier 140 may comprise similar kinds of functions andmethods as described in the context of the signal processing methods ofFIG. 29. For example, the recursive least squares method described inthe context of FIG. 29 may be used to identify the noise component ofthe measured ECG signal.

FIGS. 32 through 35 show the effect of using the method of FIG. 31 toestimate a pig's actual ECG when the ECG is measured during chestcompressions. For all four graphs each time marker 150 along time scale151 corresponds to the same time marker in the other three graphs, thusmaking possible a direct comparison of one graph to each of the othergraphs. However, the voltage scales 152 of FIGS. 32, 34, and 35 areslightly different from each other.

FIG. 32 is a graph (millivolts versus milliseconds) of an actual pig'sECG signal that is corrupted by noise caused by chest compressions. FIG.32 represents the ECG measured during compressions without signalprocessing.

FIG. 33 is a graph of force versus time for an actual CPR motion signal.The motion signal comprises a time varying force signal and correspondsto the force a CPR device places on the pig's chest while performingchest compressions. The force peaks 153 correspond to the maximum depthof compressions. In the case of a compression monitor, the motion signalcould comprise a time varying force signal that corresponds to the forceplaced on the patient's chest while a rescuer or automatic CPR deviceperforms chest compressions. In this case a force transducer disposed ona compression monitor (such as on the back of the compression monitor)measures the force of compressions and produces the force signal. Theforce signal is later correlated to the ECG noise. The force transduceror other force sensor may also be disposed under the patient's back andthen operably connected to the compression monitor.

FIG. 34 is a graph of voltage versus time for the estimated ECG noisesignal caused by the chest compressions shown in FIG. 33. A comparisonof FIGS. 33 and 34 shows that the time varying pressure signalcorresponds directly to incidence of ECG noise. In other words, thepressure peaks 153 caused by chest compressions correspond to theincidence of noise peaks 154.

The system identifier 140 used to generate the estimated noise componentof the noisy ECG comprises a recursive least squares method as describedin the context of FIG. 29. The autoregressive order was selected to beequal to 1. The moving average order was selected to be 10. Theautoregressive order was selected to be 10. The derivative order wasalso selected to be 0. (The derivative order is a linear or non-linearterm used in the system model; specifically it may be either a truncatedpositive derivative or a truncated negative derivative. The non-linearterms are extensions of the recursive least squares model fitalgorithm). The forgetting factor, λ, was selected to be 1.0000.

FIG. 35 is a graph of the pig's estimated actual ECG signal. The graphof FIG. 35 is generated by subtracting the estimated noise signal ofFIG. 34 from the measured ECG signal of FIG. 32. The estimated actualECG signal corresponds closely to the pig's actual ECG signal.

The signal processing methods described in the context of noisy ECGsignals (FIGS. 30 and 31) and noisy acceleration signals (FIGS. 28 and29), as well as the techniques described in relation to the baselinelimiter and the peak limiter, may also be used to estimate the actualvalue of the patient's transthoracic impedance (the chest's electricalresistance or impedance). The estimated actual value of the patient'stransthoracic impedance may be used to determine the amount of voltageneeded to shock the patient with a defibrillator.

As compressions are applied to the patient the measured value of thetransthoracic impedance becomes noisy. The general signal processingsolutions and the limiters already described may be used to identify,isolate, and eliminate the noise component of the measured transthoracicimpedance. Thus, the actual value of the transthoracic impedance may beestimated.

The estimated actual value of the transthoracic impedance may beprovided to a means for defibrillating the patient. The means fordefibrillating the patient uses the estimated actual value of thetransthoracic impedance to determine the exact voltage necessary toapply an effective shock to the patient. Since the exact value of thevoltage required is also known, the defibrillator can be usedefficiently (thus preserving battery life and making the device safer).

Since both the estimated actual ECG and the estimated actualtransthoracic impedance are known, an automated CPR device equipped withan AED (automated external defibrillator) may perform defibrillationshocks to a patient without stopping compressions. The device maydetermine when defibrillation is appropriate based on the estimatedactual ECG and may apply the appropriate defibrillation voltage based onthe estimated actual transthoracic impedance. Since compressions do notstop during defibrillation, the patient's blood flow does not stop(meaning the patient is more likely to survive).

The compression monitor using these signal processing techniques (foreither chest depth measurement or ECG measurement) may be used with anymeans for compressing the chest of the patient. A means for compressingthe chest may comprise manual CPR, electro-stimulation, a means forperforming automatic CPR (including belts, straps, pistons, and platesthat are driven by motors or manual levers), or other devices suitablefor compressing the chest. Examples of automatic CPR devices may befound in our own patent Sherman et al., Modular CPR Assist Device, U.S.Pat. No. 6,066,106 (May 23, 2000) and in our application CPR AssistDevice with Pressure Bladder Feedback, U.S. application Ser. No.09/866,377 filed May 25, 2001. (Both devices use optical or rotaryencoders disposed on a compression belt or a drive shaft or spool tomeasure the amount of belt pay out or compression depth). Theaccelerometer itself may be disposed in any location where theaccelerometer experiences the downward or upward acceleration ofcompressions. For example, the accelerometer may be disposed within orotherwise disposed on the means for compressing the chest, such as acompression belt. (In the case of manual compressions, the compressionmonitor may be disposed beneath a rescuer's hands while the rescuerperforms compressions, or the compression monitor may be otherwisedisposed on the rescuer's hands, wrist, or arms.)

If the compression monitor is provided with a means for sensing the tiltof the accelerometer, such as a three-axis accelerometer, three-axisload sensor, three-axis displacement measurement device, or the tiltsensor shown in our own U.S. Pat. No. 6,390,996 to Halperin et al., thenthe user feedback system can prompt the rescuer with respect tocompression efficiency. For most patients, compressions are mostefficient when compressions are performed perpendicular to the sternum(straight down in most cases). The tilt sensor measures the angle atwhich compressions are performed and the compression monitor prompts therescuer to adjust the angle if the angle falls outside a particularrange.

The compression monitor and signal processor may also be operablyconnected to a defibrillator. While the rescuer or device is performingcompressions the defibrillator or compression monitor tracks thepatient's ECG. If the compression monitor's processor measures an ECGsignal that indicates that the patient would benefit from a shock, thenthe rescuer would be instructed to apply a defibrillation shock or toallow an AED to administer a shock. A means for estimating the patient'sactual ECG during compressions comprises our own method disclosed inU.S. Pat. No. 6,390,996 to Halperin et al and the method disclosed inthis application.

The compression monitor may also be operably connected to a means forperforming ventilation. After the rescuer has performed an appropriatenumber of compressions, such as 15, the compression monitor willinstruct the rescuer to pause compressions. The means for performingventilation will then administer an appropriate number of ventilations.After ventilations the compression monitor may evaluate the patient'scondition. If the patient still requires compressions, then thecompression monitor will instruct the rescuer to resume compressions.The means for performing ventilation may comprise the rescuer, a bag orballoon, a positive pressure ventilator, an electro-ventilator such asthose shown in our own patent Sherman et al., Chest Compression Devicewith Electro-Stimulation, U.S. Pat. No. 6,213,960 (Apr. 10, 2001), orother means for performing ventilation.

The compression monitor may also be provided with a means forcommunication that allows the compression monitor to communicate with aremote network. The means for communication comprises a signal carrierand a compression monitor communicator. The signal carrier may comprisea telephone line, direct connection cables, a dedicated digital network,a cell phone network, a satellite communication array, radio or otherelectromagnetic waves, a LED, the internet, or other means for carryinga signal. The compression monitor communicator may comprise a radio orother electromagnetic wave transmitter and receiver, a LED, a modem, orother means for transmitting and receiving a signal carrier. The meansfor communication allows the compression monitor to upload or downloadinformation from a remote network. The compression monitor may also beprovided with a global positioning satellite reader (GPS reader),speakers, keypads, telephones, modems, microphones, cameras, or visualdisplays to allow the user to receive and input information. Data may beexchanged by the means for communication using known communicationstandards, such as the bluetooth standard.

The remote network may provide other information to the compressionmonitor and may also receive information from the compression monitor.For example, the compression monitor may be updated with additionalwaveforms, patient treatment history, ventilation ratios, or othercompression-related information of use in a subsequent or currentemergency. In the case when a group of monitors are assigned to a singlenetwork, each compression monitor may be tracked separately by theremote network and provided with different information (if warranted).The remote network itself may comprise one or more computers, theinternet, another CPR-related device, or a human operator capable ofremotely programming the compression monitor or remotely prompting therescuer.

In use, the compression monitor is provided at a location, such as ashopping mall or a public place, and is stored until needed. Uponactivation, the compression monitor establishes communication between itand the remote network, which may be a computer located in a callcenter. Emergency responders, such as police, fire, and ambulanceservices, may be notified of the activation and directed to go to alocation pinpointed by the GPS reader or to contact the personactivating the compression monitor. The remote network computer and anoperator trained to use the computer may provide voice assistance to therescuer while monitoring real time streaming data from the compressionmonitor. The operator or computer may from time to time provide otherinformation to the compression monitor or rescuer. For example, thecompression monitor may be provided with data corresponding to whatwaveform the rescuer should be prompted to perform and the operator mayverbally coach the rescuer. In turn, the compression monitor providesdata, such as compression depth, rate, force, waveform, and duty cycle,to the operator and computer for medical analysis. Should the rescuerbecome fatigued then the operator can provide a substitute waveform thatis easier for the rescuer to follow. Likewise, the operator can provideverbal encouragement to the rescuer.

Another use for the means for communication is to enable automatic orprompted maintenance of the compression monitor. Either periodically orcontinuously the compression monitor communicates with a remote networkand transmits data such as battery life and status, number of uses,whether any parts need to be replaced, and other information concerningmaintenance status. In response, the compression monitor eitherautomatically performs maintenance on itself, such as a softwareupgrade, or it prompts a user to perform maintenance upon it. Anotheruse for the means for communication is to enable the compression monitorto communicate with additional products used during an emergencyresponse. Example of such a products include those products describedabove, a drug dispenser, or any other product useful for responding tothe emergency.

An example of combined product use begins with a rescuer beginningmanual resuscitation with the compression monitor. Emergency medicalpersonnel arrive and deploy an automated chest compression deviceequipped with an AED. The automated chest compression device is adaptedto exchange information with the compression monitor. After theautomated chest compression device is deployed, the compression monitorautomatically communicates with the chest compression device andtransfers relevant treatment history to it, such as time undercompression, quality of compressions as compared to an ideal waveform,ECG history, and other relevant medical data. Based on the informationprovided by the compression monitor, the automated chest compressiondevice may provide a defibrillation shock to the patient beforebeginning compressions. Conversely, the automated chest compressiondevice may determine, based on the transferred data, that chestcompressions must be continued before administering a shock.

Another example of a combined product is a compression monitor and aseparate signal processor. The signal processor may be provided as oneor more physical chips (hardware) or may be provided as a computerprogram (software). In either case, the signal processor may be aseparate unit or module and need not be built directly into the monitor.Accordingly, the signal processing units may be provided as stand-aloneproducts. Thus, while the preferred embodiments of the devices andmethods have been described in reference to the environment in whichthey were developed, they are merely illustrative of the principles ofthe inventions. Other embodiments and configurations may be devisedwithout departing from the spirit of the inventions and the scope of theappended claims.

1. A device for compressing a chest of a patient, said devicecomprising: an automated device for performing chest compressions on apatient; a means for performing defibrillation operably connected to theautomated device for performing chest compressions; a means for sensingthe ECG signal of the patient, said means for sensing the ECG signalcapable of producing a measured ECG signal corresponding to the measuredvalue of the ECG signal of the patient, wherein the ECG signal comprisesan actual component and a noise component; a compression sensor operablyconnected to the automated device for performing chest compressions,said compression sensor capable of producing a compression signalcorresponding to the presence of a chest compression; a processoroperably connected to the compression sensor and to the means forsensing the ECG signal, said processor capable of producing an estimatedactual ECG signal corresponding to the estimated actual ECG signal ofthe patient and capable of performing defibrillation shocks to thepatient using the means for defibrillation without stopping chestcompressions being performed by the automated device for performingchest compressions; wherein the compression sensor comprises a loadsensor disposed beneath the patient which senses a load whencompressions begin.
 2. The device of claim 1 wherein the compressionsensor comprises a means for measuring the displacement of a compressionbelt.
 3. The device of claim 2 wherein the means for measuring thedisplacement of a compression belt comprises an encoder.
 4. The deviceof claim 3 wherein the encoder comprises a rotary encoder.
 5. The deviceof claim 3 wherein the encoder comprises an optical encoder.
 6. Thedevice of claim 1 wherein the compression sensor comprises anaccelerometer.
 7. The device of claim 1 wherein defibrillation shocksare performed on the patient by the means for defibrillation based onthe estimated actual ECG signal.
 8. The device of claim 7 wherein avoltage level of the defibrillation shocks is based on the estimatedactual ECG signal.
 9. The device of claim 1 further comprising a displayoperably connected to the processor wherein the display is capable ofdisplaying the estimated actual ECG signal.
 10. The device of claim 1further comprising a means for user feedback operably connected to theprocessor, wherein the means for user feedback is capable of providingfeedback that indicates whether the patient requires defibrillation. 11.The device of claim 1 wherein the a processor is programmed to estimatea value of the patient's transthoracic impedance and use the value ofthe patient's transthoracic impedance to determine an amount of energyused to shock the patient with the defibrillator.
 12. A method ofperforming CPR on a patient, wherein the method comprises the steps of:providing a CPR device comprising an automated device for performingchest compressions on a patient and a means for performingdefibrillation; wherein the device is capable of measuring an ECG signalof the patient during compressions, said ECG signal comprising a noisecomponent and an actual component and of producing; wherein the deviceis further capable of determining an estimated actual ECG signal duringthe chest compression of the patient; performing chest compressions onthe patient with the automated CPR device; producing the estimatedactual ECG signal during the chest compressing; and applying adefibrillation shock to the patient during the chest compression basedon the estimated actual ECG signal; providing a load sensor disposedbeneath the patient which is capable of identifying the start of acompression; identifying the start of a compression with the loadsensor; and calculating estimated actual depth of compressions when thestart of a compression has been identified.